Abstract Real-time passenger monitoring in resource-constrained ferry terminals demands low-latency deep learning inference under severe occlusion, high crowd density, and variable lighting conditions. This paper presents a structured YOLOv8 development pipeline for edge deployment on single-board computers (SBCs) that addresses these computer vision challenges in public transportation IoT systems. Trained on a custom 2229-image dataset augmented from EMAP surveillance footage, the model achieves 97% person-detection precision in production conditions, even with 5 classes. Comprehensive benchmarking across Raspberry Pi 3/4B and Jetson Nano reveals critical hardware trade-offs: CPU-only SBCs fail real-time requirements, while Jetson Nano delivers 5.3 ms/image inference via Maxwell GPU acceleration. These findings validate state-of-the-art CNN feasibility for edge IoT, quantifying performance-resource boundaries essential for safety-critical transportation infrastructure. The analysis guides SBC selection for scalable, low-power vision systems.
Reis et al. (Thu,) studied this question.